Import functions and models

load('../Data/GER/ger_list_results_fixed_window.RData')
load('../Data/US/us_list_results_fixed_window.RData')

library(lmtest)
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
library(car)
## Loading required package: carData
library(survival)
library(tidyverse)
## ── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.0     ✓ purrr   0.3.3
## ✓ tibble  3.0.0     ✓ dplyr   0.8.5
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## Warning: package 'tibble' was built under R version 3.6.2
## ── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
## x dplyr::recode() masks car::recode()
## x purrr::some()   masks car::some()

Define Functions

list_iterater <- function(models, test) {
  for(i in models){
    for(j in i){
      
      if(test == 'qq'){j %>% plot(2)}
      if(test == 'ks'){j %>% resid() %>% ks.test(y=pnorm) %>% print()}
      if(test == 'bp'){j %>% bptest() %>% print()}
      if(test == 'ph'){j %>% cox.zph() %>% print()}
    }
  }
}

Assumptions GER COVID-19 onsets (proportional hazards)

list_iterater(ger_list_results$ger_cox_prev_onset, test = 'ph')
##        chisq df     p
## pers    4.52  1 0.034
## GLOBAL  4.52  1 0.034
##              chisq df       p
## pers          2.76  1 0.09691
## age          14.28  1 0.00016
## male          2.95  1 0.08600
## conservative  8.68  1 0.00322
## GLOBAL       16.67  4 0.00224
##            chisq df    p
## pers      0.7311  1 0.39
## academics 0.1784  1 0.67
## medinc    0.1109  1 0.74
## manufact  0.0784  1 0.78
## GLOBAL    0.9387  4 0.92
##              chisq df     p
## pers         5.231  1 0.022
## airport_dist 1.756  1 0.185
## tourism      1.683  1 0.195
## healthcare   0.249  1 0.617
## popdens      3.738  1 0.053
## GLOBAL       9.389  5 0.095
##                chisq df       p
## pers          1.2095  1   0.271
## age          16.1514  1 5.8e-05
## male          3.8894  1   0.049
## conservative  5.4494  1   0.020
## academics     1.0705  1   0.301
## medinc        1.4807  1   0.224
## manufact      0.1070  1   0.744
## airport_dist  0.6959  1   0.404
## tourism       0.5836  1   0.445
## healthcare    0.0349  1   0.852
## popdens       2.4390  1   0.118
## GLOBAL       23.2077 11   0.017
##        chisq df    p
## pers    1.33  1 0.25
## GLOBAL  1.33  1 0.25
##                chisq df      p
## pers          0.0073  1 0.9319
## age          13.8084  1 0.0002
## male          3.3349  1 0.0678
## conservative  7.9125  1 0.0049
## GLOBAL       16.8658  4 0.0021
##            chisq df    p
## pers      0.0186  1 0.89
## academics 0.1976  1 0.66
## medinc    0.0389  1 0.84
## manufact  0.0418  1 0.84
## GLOBAL    0.2905  4 0.99
##              chisq df     p
## pers         0.882  1 0.348
## airport_dist 2.401  1 0.121
## tourism      1.541  1 0.214
## healthcare   0.642  1 0.423
## popdens      3.038  1 0.081
## GLOBAL       6.928  5 0.226
##               chisq df       p
## pers          0.397  1   0.528
## age          17.013  1 3.7e-05
## male          3.788  1   0.052
## conservative  5.717  1   0.017
## academics     1.012  1   0.314
## medinc        1.422  1   0.233
## manufact      0.154  1   0.695
## airport_dist  0.918  1   0.338
## tourism       0.535  1   0.464
## healthcare    0.020  1   0.888
## popdens       2.299  1   0.129
## GLOBAL       23.750 11   0.014
##        chisq df       p
## pers    14.6  1 0.00013
## GLOBAL  14.6  1 0.00013
##              chisq df       p
## pers         12.12  1 0.00050
## age          13.67  1 0.00022
## male          2.86  1 0.09069
## conservative  7.37  1 0.00664
## GLOBAL       24.38  4 6.7e-05
##            chisq df      p
## pers      8.5445  1 0.0035
## academics 0.2101  1 0.6467
## medinc    0.0319  1 0.8582
## manufact  0.0658  1 0.7976
## GLOBAL    8.9481  4 0.0624
##              chisq df       p
## pers         13.32  1 0.00026
## airport_dist  1.39  1 0.23911
## tourism       1.84  1 0.17483
## healthcare    1.19  1 0.27596
## popdens       2.64  1 0.10430
## GLOBAL       17.47  5 0.00370
##                 chisq df       p
## pers          9.22198  1  0.0024
## age          15.77702  1 7.1e-05
## male          3.84206  1  0.0500
## conservative  5.13675  1  0.0234
## academics     1.01781  1  0.3130
## medinc        1.77555  1  0.1827
## manufact      0.09080  1  0.7632
## airport_dist  0.62927  1  0.4276
## tourism       0.60674  1  0.4360
## healthcare    0.00677  1  0.9344
## popdens       2.35550  1  0.1248
## GLOBAL       28.20277 11  0.0030
##        chisq df      p
## pers    7.06  1 0.0079
## GLOBAL  7.06  1 0.0079
##              chisq df       p
## pers          4.24  1 0.03957
## age          13.76  1 0.00021
## male          2.99  1 0.08388
## conservative  7.79  1 0.00527
## GLOBAL       20.59  4 0.00038
##            chisq df     p
## pers      2.8541  1 0.091
## academics 0.0515  1 0.821
## medinc    0.1330  1 0.715
## manufact  0.0353  1 0.851
## GLOBAL    3.2582  4 0.516
##              chisq df      p
## pers          7.26  1 0.0071
## airport_dist  1.76  1 0.1840
## tourism       1.76  1 0.1842
## healthcare    0.69  1 0.4061
## popdens       2.84  1 0.0921
## GLOBAL       12.70  5 0.0264
##                chisq df       p
## pers          2.9407  1  0.0864
## age          15.9577  1 6.5e-05
## male          3.9656  1  0.0464
## conservative  5.3042  1  0.0213
## academics     0.8371  1  0.3602
## medinc        1.4335  1  0.2312
## manufact      0.1499  1  0.6986
## airport_dist  0.6402  1  0.4236
## tourism       0.5696  1  0.4504
## healthcare    0.0299  1  0.8627
## popdens       2.3287  1  0.1270
## GLOBAL       26.9323 11  0.0047
##        chisq df     p
## pers    13.8  1 2e-04
## GLOBAL  13.8  1 2e-04
##              chisq df       p
## pers          9.71  1 0.00183
## age          12.19  1 0.00048
## male          2.53  1 0.11158
## conservative  7.65  1 0.00569
## GLOBAL       24.19  4 7.3e-05
##            chisq df    p
## pers      2.5946  1 0.11
## academics 0.0986  1 0.75
## medinc    0.4625  1 0.50
## manufact  0.1113  1 0.74
## GLOBAL    3.4193  4 0.49
##              chisq df      p
## pers         13.83  1 0.0002
## airport_dist  1.63  1 0.2013
## tourism       1.69  1 0.1939
## healthcare    0.43  1 0.5120
## popdens       3.70  1 0.0543
## GLOBAL       17.72  5 0.0033
##                 chisq df       p
## pers          2.26904  1 0.13198
## age          14.32973  1 0.00015
## male          3.46706  1 0.06260
## conservative  5.06956  1 0.02435
## academics     0.92726  1 0.33558
## medinc        0.68421  1 0.40814
## manufact      0.00395  1 0.94989
## airport_dist  0.59959  1 0.43873
## tourism       0.51675  1 0.47223
## healthcare    0.08204  1 0.77455
## popdens       2.72761  1 0.09863
## GLOBAL       23.50172 11 0.01501

Assumptions US COVID-19 onsets (proportional hazards)

list_iterater(us_list_results$us_cox_prev_onset, test = 'ph')
##        chisq df       p
## pers    61.6  1 4.2e-15
## GLOBAL  61.6  1 4.2e-15
##                chisq df       p
## pers         40.6084  1 1.9e-10
## age           0.0204  1  0.8866
## male         10.7162  1  0.0011
## conservative 75.4477  1 < 2e-16
## GLOBAL       96.5136  4 < 2e-16
##           chisq df       p
## pers       40.3  1 2.2e-10
## academics 107.9  1 < 2e-16
## medinc     47.8  1 4.7e-12
## manufact   55.1  1 1.1e-13
## GLOBAL    123.7  4 < 2e-16
##               chisq df       p
## pers         42.234  1 8.1e-11
## airport_dist  0.916  1 0.33846
## tourism       6.455  1 0.01106
## healthcare   49.690  1 1.8e-12
## popdens      13.509  1 0.00024
## GLOBAL       73.998  5 1.5e-14
##                 chisq df       p
## pers         3.15e+01  1 2.0e-08
## age          7.94e-04  1 0.97752
## male         1.17e+01  1 0.00063
## conservative 7.40e+01  1 < 2e-16
## academics    9.68e+01  1 < 2e-16
## medinc       4.17e+01  1 1.1e-10
## manufact     4.46e+01  1 2.4e-11
## airport_dist 5.13e-03  1 0.94290
## tourism      6.10e+00  1 0.01353
## healthcare   4.91e+01  1 2.4e-12
## popdens      3.72e+01  1 1.1e-09
## GLOBAL       1.43e+02 11 < 2e-16
##        chisq df    p
## pers    6.57  1 0.01
## GLOBAL  6.57  1 0.01
##                chisq df       p
## pers          10.597  1 0.00113
## age            0.322  1 0.57065
## male          10.951  1 0.00094
## conservative  86.523  1 < 2e-16
## GLOBAL       107.274  4 < 2e-16
##            chisq df       p
## pers        3.85  1    0.05
## academics 115.52  1 < 2e-16
## medinc     52.02  1 5.5e-13
## manufact   56.96  1 4.5e-14
## GLOBAL    128.68  4 < 2e-16
##               chisq df       p
## pers          7.462  1 0.00630
## airport_dist  0.856  1 0.35481
## tourism       6.983  1 0.00823
## healthcare   53.282  1 2.9e-13
## popdens      13.292  1 0.00027
## GLOBAL       58.576  5 2.4e-11
##                 chisq df       p
## pers         6.68e+00  1  0.0097
## age          1.48e-04  1  0.9903
## male         1.05e+01  1  0.0012
## conservative 7.81e+01  1 < 2e-16
## academics    1.01e+02  1 < 2e-16
## medinc       4.54e+01  1 1.6e-11
## manufact     4.55e+01  1 1.5e-11
## airport_dist 9.26e-02  1  0.7609
## tourism      6.47e+00  1  0.0110
## healthcare   5.33e+01  1 2.9e-13
## popdens      3.46e+01  1 4.0e-09
## GLOBAL       1.52e+02 11 < 2e-16
##        chisq df     p
## pers    2.74  1 0.098
## GLOBAL  2.74  1 0.098
##               chisq df      p
## pers          1.953  1 0.1623
## age           0.322  1 0.5704
## male         12.545  1 0.0004
## conservative 89.021  1 <2e-16
## GLOBAL       95.248  4 <2e-16
##            chisq df       p
## pers        1.91  1    0.17
## academics 114.78  1 < 2e-16
## medinc     53.16  1 3.1e-13
## manufact   58.62  1 1.9e-14
## GLOBAL    130.14  4 < 2e-16
##              chisq df       p
## pers          1.56  1 0.21152
## airport_dist  1.09  1 0.29679
## tourism       6.49  1 0.01086
## healthcare   55.32  1   1e-13
## popdens      14.63  1 0.00013
## GLOBAL       58.99  5   2e-11
##                 chisq df       p
## pers           1.0607  1 0.30305
## age            0.0267  1 0.87009
## male          12.7667  1 0.00035
## conservative  79.8980  1 < 2e-16
## academics    100.7565  1 < 2e-16
## medinc        44.6123  1 2.4e-11
## manufact      46.3450  1 9.9e-12
## airport_dist   0.0107  1 0.91751
## tourism        6.1069  1 0.01347
## healthcare    52.4435  1 4.4e-13
## popdens       38.5603  1 5.3e-10
## GLOBAL       152.4888 11 < 2e-16
##        chisq df     p
## pers    2.83  1 0.092
## GLOBAL  2.83  1 0.092
##                chisq df       p
## pers           4.677  1 0.03056
## age            0.373  1 0.54131
## male          11.923  1 0.00055
## conservative  88.977  1 < 2e-16
## GLOBAL       109.853  4 < 2e-16
##             chisq df       p
## pers        0.671  1    0.41
## academics 116.350  1 < 2e-16
## medinc     53.619  1 2.4e-13
## manufact   61.091  1 5.5e-15
## GLOBAL    132.575  4 < 2e-16
##              chisq df       p
## pers          1.86  1  0.1729
## airport_dist  1.20  1  0.2726
## tourism       6.72  1  0.0096
## healthcare   56.55  1 5.5e-14
## popdens      16.63  1 4.5e-05
## GLOBAL       59.33  5 1.7e-11
##                 chisq df       p
## pers         1.47e+00  1 0.22600
## age          2.97e-02  1 0.86308
## male         1.16e+01  1 0.00066
## conservative 8.27e+01  1 < 2e-16
## academics    1.04e+02  1 < 2e-16
## medinc       4.57e+01  1 1.4e-11
## manufact     4.82e+01  1 3.8e-12
## airport_dist 6.07e-03  1 0.93791
## tourism      6.20e+00  1 0.01274
## healthcare   5.53e+01  1 1.0e-13
## popdens      4.17e+01  1 1.1e-10
## GLOBAL       1.56e+02 11 < 2e-16
##        chisq df       p
## pers    28.6  1 8.7e-08
## GLOBAL  28.6  1 8.7e-08
##                chisq df       p
## pers         16.1841  1 5.7e-05
## age           0.0996  1   0.752
## male         10.8343  1   0.001
## conservative 86.3413  1 < 2e-16
## GLOBAL       95.2857  4 < 2e-16
##           chisq df       p
## pers       25.7  1 4.0e-07
## academics 120.1  1 < 2e-16
## medinc     56.3  1 6.2e-14
## manufact   56.0  1 7.4e-14
## GLOBAL    135.3  4 < 2e-16
##               chisq df       p
## pers         19.913  1 8.1e-06
## airport_dist  0.332  1 0.56452
## tourism       5.085  1 0.02414
## healthcare   57.477  1 3.4e-14
## popdens      12.933  1 0.00032
## GLOBAL       74.240  5 1.3e-14
##                 chisq df       p
## pers         1.44e+01  1 0.00015
## age          4.65e-03  1 0.94564
## male         1.13e+01  1 0.00078
## conservative 7.77e+01  1 < 2e-16
## academics    1.04e+02  1 < 2e-16
## medinc       4.79e+01  1 4.4e-12
## manufact     4.48e+01  1 2.2e-11
## airport_dist 6.10e-02  1 0.80493
## tourism      5.28e+00  1 0.02151
## healthcare   5.43e+01  1 1.7e-13
## popdens      3.43e+01  1 4.8e-09
## GLOBAL       1.56e+02 11 < 2e-16

Assumptions GER COVID-19 growth rates (normality of residuals)

list_iterater(ger_list_results$ger_lm_prev_slope, test = 'qq')

list_iterater(ger_list_results$ger_lm_prev_slope, test = 'bp')
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.10128, df = 1, p-value = 0.7503
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 1.9618, df = 4, p-value = 0.7428
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 14.782, df = 4, p-value = 0.005176
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 9.2165, df = 5, p-value = 0.1007
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 24.657, df = 11, p-value = 0.01023
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.40351, df = 1, p-value = 0.5253
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.0729, df = 4, p-value = 0.7223
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 14.461, df = 4, p-value = 0.005961
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 9.9498, df = 5, p-value = 0.07667
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 24.132, df = 11, p-value = 0.01219
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.013425, df = 1, p-value = 0.9078
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 1.9958, df = 4, p-value = 0.7365
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 12.444, df = 4, p-value = 0.01434
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 9.7153, df = 5, p-value = 0.08372
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 24.05, df = 11, p-value = 0.01253
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.0338, df = 1, p-value = 0.1538
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 1.7397, df = 4, p-value = 0.7835
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 13.801, df = 4, p-value = 0.007958
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 16.165, df = 5, p-value = 0.006389
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 24.197, df = 11, p-value = 0.01193
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.0091219, df = 1, p-value = 0.9239
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.281, df = 4, p-value = 0.6842
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 13.807, df = 4, p-value = 0.007938
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 12.565, df = 5, p-value = 0.02782
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 25.207, df = 11, p-value = 0.008503

Assumptions US COVID-19 growth rates (normality of residuals)

list_iterater(us_list_results$us_lm_prev_slope, test = 'qq')

list_iterater(us_list_results$us_lm_prev_slope, test = 'bp')
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 27.212, df = 1, p-value = 1.823e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 61.721, df = 4, p-value = 1.261e-12
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 28.513, df = 4, p-value = 9.818e-06
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 91.398, df = 5, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 124.17, df = 11, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.56555, df = 1, p-value = 0.452
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 61.782, df = 4, p-value = 1.224e-12
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 37.796, df = 4, p-value = 1.235e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 98.721, df = 5, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 132.44, df = 11, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 3.709, df = 1, p-value = 0.05412
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 62.437, df = 4, p-value = 8.914e-13
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 21.864, df = 4, p-value = 0.0002133
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 88.004, df = 5, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 122.68, df = 11, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 7.4312, df = 1, p-value = 0.00641
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 68.758, df = 4, p-value = 4.15e-14
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 60.223, df = 4, p-value = 2.604e-12
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 115.14, df = 5, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 153.09, df = 11, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 13.367, df = 1, p-value = 0.000256
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 61.101, df = 4, p-value = 1.702e-12
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 33.401, df = 4, p-value = 9.887e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 97.548, df = 5, p-value < 2.2e-16
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 127.31, df = 11, p-value < 2.2e-16

Assumptions GER socdist onsets

list_iterater(ger_list_results$ger_cox_socdist_cpt, test = 'ph')
##        chisq df    p
## pers   0.404  1 0.52
## GLOBAL 0.404  1 0.52
##              chisq df      p
## pers         1.154  1 0.2827
## age          1.266  1 0.2604
## male         7.065  1 0.0079
## conservative 0.874  1 0.3499
## GLOBAL       9.658  4 0.0466
##            chisq df     p
## pers      0.2426  1 0.622
## academics 0.0241  1 0.877
## medinc    4.6830  1 0.030
## manufact  4.8907  1 0.027
## GLOBAL    7.6983  4 0.103
##                chisq df     p
## pers         0.21564  1 0.642
## airport_dist 2.72244  1 0.099
## tourism      0.00569  1 0.940
## healthcare   1.45086  1 0.228
## popdens      0.01079  1 0.917
## GLOBAL       4.20382  5 0.520
##                 chisq df      p
## pers          0.46887  1 0.4935
## age           3.34315  1 0.0675
## male          7.50922  1 0.0061
## conservative  2.19579  1 0.1384
## academics     0.00651  1 0.9357
## medinc        3.57194  1 0.0588
## manufact      2.06999  1 0.1502
## airport_dist  5.44624  1 0.0196
## tourism       0.52194  1 0.4700
## healthcare    2.58597  1 0.1078
## popdens       0.10598  1 0.7448
## onset_prev    3.34065  1 0.0676
## slope_prev    4.71555  1 0.0299
## GLOBAL       16.82209 13 0.2076
##        chisq df    p
## pers   0.159  1 0.69
## GLOBAL 0.159  1 0.69
##              chisq df      p
## pers         0.854  1 0.3554
## age          1.073  1 0.3003
## male         6.922  1 0.0085
## conservative 0.676  1 0.4108
## GLOBAL       8.157  4 0.0860
##            chisq df     p
## pers      0.3156  1 0.574
## academics 0.0588  1 0.808
## medinc    4.0703  1 0.044
## manufact  5.0115  1 0.025
## GLOBAL    6.3765  4 0.173
##                chisq df     p
## pers         0.03622  1 0.849
## airport_dist 2.93476  1 0.087
## tourism      0.00399  1 0.950
## healthcare   1.88139  1 0.170
## popdens      0.07296  1 0.787
## GLOBAL       5.30803  5 0.379
##                 chisq df     p
## pers          0.31735  1 0.573
## age           3.26441  1 0.071
## male          7.27168  1 0.007
## conservative  2.12924  1 0.145
## academics     0.00213  1 0.963
## medinc        3.49245  1 0.062
## manufact      2.05690  1 0.152
## airport_dist  5.80195  1 0.016
## tourism       0.46195  1 0.497
## healthcare    2.80165  1 0.094
## popdens       0.04351  1 0.835
## onset_prev    3.42436  1 0.064
## slope_prev    4.75551  1 0.029
## GLOBAL       16.22651 13 0.237
##        chisq df   p
## pers   0.154  1 0.7
## GLOBAL 0.154  1 0.7
##              chisq df      p
## pers         0.404  1 0.5249
## age          0.836  1 0.3604
## male         6.729  1 0.0095
## conservative 0.550  1 0.4583
## GLOBAL       7.606  4 0.1071
##            chisq df     p
## pers      0.0844  1 0.771
## academics 0.0204  1 0.886
## medinc    3.9203  1 0.048
## manufact  4.0666  1 0.044
## GLOBAL    5.8512  4 0.211
##               chisq df    p
## pers         0.1585  1 0.69
## airport_dist 2.6285  1 0.10
## tourism      0.0106  1 0.92
## healthcare   1.4996  1 0.22
## popdens      0.0151  1 0.90
## GLOBAL       4.1465  5 0.53
##                chisq df      p
## pers          0.0442  1 0.8334
## age           2.9245  1 0.0872
## male          7.3914  1 0.0066
## conservative  2.0411  1 0.1531
## academics     0.0117  1 0.9139
## medinc        2.5679  1 0.1091
## manufact      1.4444  1 0.2294
## airport_dist  5.7027  1 0.0169
## tourism       0.6132  1 0.4336
## healthcare    2.9742  1 0.0846
## popdens       0.0553  1 0.8142
## onset_prev    2.7730  1 0.0959
## slope_prev    4.2054  1 0.0403
## GLOBAL       16.9939 13 0.1996
##        chisq df    p
## pers   0.134  1 0.71
## GLOBAL 0.134  1 0.71
##              chisq df     p
## pers         0.584  1 0.445
## age          1.041  1 0.308
## male         6.496  1 0.011
## conservative 0.690  1 0.406
## GLOBAL       7.954  4 0.093
##           chisq df    p
## pers      0.233  1 0.63
## academics 0.024  1 0.88
## medinc    4.215  1 0.04
## manufact  4.696  1 0.03
## GLOBAL    6.208  4 0.18
##                chisq df     p
## pers         0.04716  1 0.828
## airport_dist 2.93708  1 0.087
## tourism      0.00592  1 0.939
## healthcare   1.45116  1 0.228
## popdens      0.01290  1 0.910
## GLOBAL       4.78550  5 0.443
##                 chisq df      p
## pers         6.07e-01  1 0.4358
## age          3.25e+00  1 0.0712
## male         7.14e+00  1 0.0076
## conservative 2.10e+00  1 0.1477
## academics    7.84e-04  1 0.9777
## medinc       3.48e+00  1 0.0622
## manufact     1.99e+00  1 0.1580
## airport_dist 5.89e+00  1 0.0152
## tourism      4.97e-01  1 0.4807
## healthcare   2.62e+00  1 0.1053
## popdens      9.50e-02  1 0.7579
## onset_prev   3.32e+00  1 0.0684
## slope_prev   4.81e+00  1 0.0283
## GLOBAL       1.64e+01 13 0.2283
##        chisq df    p
## pers   0.944  1 0.33
## GLOBAL 0.944  1 0.33
##              chisq df     p
## pers         0.264  1 0.607
## age          1.201  1 0.273
## male         6.375  1 0.012
## conservative 0.843  1 0.359
## GLOBAL       7.168  4 0.127
##            chisq df     p
## pers      1.1126  1 0.292
## academics 0.0172  1 0.896
## medinc    4.8148  1 0.028
## manufact  4.9173  1 0.027
## GLOBAL    6.7084  4 0.152
##                 chisq df     p
## pers         1.024360  1 0.311
## airport_dist 2.746720  1 0.097
## tourism      0.001333  1 0.971
## healthcare   1.290102  1 0.256
## popdens      0.000404  1 0.984
## GLOBAL       4.572367  5 0.470
##                 chisq df      p
## pers          1.15693  1 0.2821
## age           3.28630  1 0.0699
## male          6.96337  1 0.0083
## conservative  2.23667  1 0.1348
## academics     0.00162  1 0.9679
## medinc        3.58681  1 0.0582
## manufact      2.04020  1 0.1532
## airport_dist  5.71785  1 0.0168
## tourism       0.43810  1 0.5080
## healthcare    2.43042  1 0.1190
## popdens       0.14832  1 0.7001
## onset_prev    3.43784  1 0.0637
## slope_prev    4.91867  1 0.0266
## GLOBAL       16.62267 13 0.2171

Assumptions US socdist onsets

list_iterater(us_list_results$us_cox_socdist_cpt, test = 'ph')
##        chisq df     p
## pers    39.7  1 3e-10
## GLOBAL  39.7  1 3e-10
##              chisq df       p
## pers         39.94  1 2.6e-10
## age           3.29  1 0.06951
## male          8.97  1 0.00274
## conservative 11.75  1 0.00061
## GLOBAL       46.79  4 1.7e-09
##            chisq df       p
## pers      35.253  1 2.9e-09
## academics 11.100  1 0.00086
## medinc     0.688  1 0.40675
## manufact   6.541  1 0.01054
## GLOBAL    37.875  4 1.2e-07
##               chisq df       p
## pers          46.23  1 1.0e-11
## airport_dist  31.52  1 2.0e-08
## tourism       26.81  1 2.2e-07
## healthcare     2.83  1   0.092
## popdens       57.82  1 2.9e-14
## GLOBAL       112.47  5 < 2e-16
##               chisq df       p
## pers          37.40  1 9.6e-10
## age            2.30  1 0.12952
## male           7.56  1 0.00596
## conservative  18.52  1 1.7e-05
## academics     14.15  1 0.00017
## medinc         1.62  1 0.20320
## manufact       6.91  1 0.00856
## airport_dist  33.36  1 7.6e-09
## tourism       21.82  1 3.0e-06
## healthcare     2.09  1 0.14845
## popdens       39.28  1 3.7e-10
## onset_prev    73.00  1 < 2e-16
## slope_prev    66.21  1 4.1e-16
## GLOBAL       136.53 13 < 2e-16
##        chisq df       p
## pers    12.3  1 0.00045
## GLOBAL  12.3  1 0.00045
##              chisq df       p
## pers         12.01  1 0.00053
## age           2.43  1 0.11932
## male          6.91  1 0.00858
## conservative  9.74  1 0.00180
## GLOBAL       26.25  4 2.8e-05
##            chisq df       p
## pers       9.834  1  0.0017
## academics  8.571  1  0.0034
## medinc     0.208  1  0.6481
## manufact   5.842  1  0.0157
## GLOBAL    26.730  4 2.3e-05
##               chisq df       p
## pers           8.88  1  0.0029
## airport_dist  33.80  1 6.1e-09
## tourism       25.76  1 3.9e-07
## healthcare     1.56  1  0.2116
## popdens       72.97  1 < 2e-16
## GLOBAL       123.12  5 < 2e-16
##                chisq df       p
## pers           8.861  1 0.00291
## age            1.502  1 0.22029
## male           6.349  1 0.01174
## conservative  17.152  1 3.5e-05
## academics     12.346  1 0.00044
## medinc         0.969  1 0.32498
## manufact       7.453  1 0.00633
## airport_dist  34.721  1 3.8e-09
## tourism       21.579  1 3.4e-06
## healthcare     1.307  1 0.25289
## popdens       40.702  1 1.8e-10
## onset_prev    69.578  1 < 2e-16
## slope_prev    66.958  1 2.8e-16
## GLOBAL       134.867 13 < 2e-16
##        chisq df    p
## pers   0.487  1 0.49
## GLOBAL 0.487  1 0.49
##              chisq df       p
## pers          0.36  1 0.54853
## age           3.07  1 0.07955
## male          7.68  1 0.00557
## conservative  9.81  1 0.00174
## GLOBAL       18.68  4 0.00091
##             chisq df      p
## pers       0.0724  1 0.7879
## academics 10.0939  1 0.0015
## medinc     0.5353  1 0.4644
## manufact   6.6588  1 0.0099
## GLOBAL    17.7158  4 0.0014
##               chisq df       p
## pers           0.40  1    0.53
## airport_dist  30.10  1 4.1e-08
## tourism       27.49  1 1.6e-07
## healthcare     2.17  1    0.14
## popdens       70.44  1 < 2e-16
## GLOBAL       114.32  5 < 2e-16
##                 chisq df       p
## pers         7.55e-04  1 0.97807
## age          1.93e+00  1 0.16431
## male         7.00e+00  1 0.00813
## conservative 1.72e+01  1 3.3e-05
## academics    1.40e+01  1 0.00019
## medinc       1.54e+00  1 0.21466
## manufact     7.44e+00  1 0.00639
## airport_dist 3.30e+01  1 9.2e-09
## tourism      2.21e+01  1 2.5e-06
## healthcare   1.73e+00  1 0.18873
## popdens      4.08e+01  1 1.6e-10
## onset_prev   7.26e+01  1 < 2e-16
## slope_prev   6.74e+01  1 < 2e-16
## GLOBAL       1.33e+02 13 < 2e-16
##        chisq df       p
## pers      16  1 6.2e-05
## GLOBAL    16  1 6.2e-05
##              chisq df       p
## pers         15.65  1 7.6e-05
## age           2.94  1  0.0867
## male          7.49  1  0.0062
## conservative  9.79  1  0.0018
## GLOBAL       28.39  4 1.0e-05
##            chisq df       p
## pers      13.423  1 0.00025
## academics  9.485  1 0.00207
## medinc     0.395  1 0.52966
## manufact   6.174  1 0.01296
## GLOBAL    32.320  4 1.6e-06
##               chisq df       p
## pers          10.82  1   0.001
## airport_dist  31.89  1 1.6e-08
## tourism       25.77  1 3.8e-07
## healthcare     1.76  1   0.184
## popdens       71.58  1 < 2e-16
## GLOBAL       127.26  5 < 2e-16
##               chisq df       p
## pers          12.51  1 0.00041
## age            1.80  1 0.17965
## male           6.80  1 0.00911
## conservative  17.11  1 3.5e-05
## academics     13.40  1 0.00025
## medinc         1.29  1 0.25537
## manufact       7.58  1 0.00591
## airport_dist  33.35  1 7.7e-09
## tourism       21.56  1 3.4e-06
## healthcare     1.47  1 0.22469
## popdens       40.61  1 1.9e-10
## onset_prev    72.23  1 < 2e-16
## slope_prev    68.35  1 < 2e-16
## GLOBAL       137.68 13 < 2e-16
##        chisq df      p
## pers     8.6  1 0.0034
## GLOBAL   8.6  1 0.0034
##              chisq df       p
## pers          8.88  1 0.00289
## age           2.07  1 0.14999
## male          7.06  1 0.00788
## conservative  9.73  1 0.00181
## GLOBAL       19.75  4 0.00056
##            chisq df       p
## pers       9.806  1 0.00174
## academics  7.980  1 0.00473
## medinc     0.118  1 0.73103
## manufact   7.012  1 0.00810
## GLOBAL    20.852  4 0.00034
##               chisq df       p
## pers           8.36  1  0.0038
## airport_dist  32.79  1 1.0e-08
## tourism       25.97  1 3.5e-07
## healthcare     1.35  1  0.2453
## popdens       62.20  1 3.1e-15
## GLOBAL       108.93  5 < 2e-16
##                chisq df       p
## pers          11.725  1 0.00062
## age            1.449  1 0.22864
## male           6.709  1 0.00959
## conservative  18.242  1 1.9e-05
## academics     12.639  1 0.00038
## medinc         0.918  1 0.33792
## manufact       8.296  1 0.00397
## airport_dist  34.192  1 5.0e-09
## tourism       22.255  1 2.4e-06
## healthcare     1.310  1 0.25232
## popdens       40.266  1 2.2e-10
## onset_prev    72.228  1 < 2e-16
## slope_prev    68.799  1 < 2e-16
## GLOBAL       138.349 13 < 2e-16

Assumptions GER socdist adjustment levels

list_iterater(ger_list_results$ger_lm_socdist_mean, test = 'qq')

list_iterater(ger_list_results$ger_lm_socdist_mean, test = 'bp')
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 4.1158, df = 1, p-value = 0.04248
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 8.8268, df = 4, p-value = 0.06558
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 37.09, df = 4, p-value = 1.726e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 1.2643, df = 5, p-value = 0.9386
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 25.778, df = 13, p-value = 0.0182
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 1.8021, df = 1, p-value = 0.1795
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 15.008, df = 4, p-value = 0.004684
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 42.745, df = 4, p-value = 1.169e-08
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 8.1449, df = 5, p-value = 0.1484
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 31.748, df = 13, p-value = 0.002618
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.549, df = 1, p-value = 0.1104
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 12.838, df = 4, p-value = 0.0121
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 37.38, df = 4, p-value = 1.504e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 4.6026, df = 5, p-value = 0.4663
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 26.394, df = 13, p-value = 0.01504
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 7.4375, df = 1, p-value = 0.006388
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 14.601, df = 4, p-value = 0.005605
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 41.909, df = 4, p-value = 1.742e-08
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 8.6427, df = 5, p-value = 0.1242
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 34.447, df = 13, p-value = 0.001029
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 2.0602, df = 1, p-value = 0.1512
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 13.623, df = 4, p-value = 0.0086
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 37.353, df = 4, p-value = 1.524e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 7.1669, df = 5, p-value = 0.2085
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 27.611, df = 13, p-value = 0.01025

Assumptions US socdist adjustment levels

list_iterater(us_list_results$us_lm_socdist_mean, test = 'qq')

list_iterater(us_list_results$us_lm_socdist_mean, test = 'bp')
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 11.76, df = 1, p-value = 0.0006051
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 11.57, df = 4, p-value = 0.02085
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 9.5642, df = 4, p-value = 0.04844
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 27.495, df = 5, p-value = 4.568e-05
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 27.97, df = 13, p-value = 0.009139
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 6.665, df = 1, p-value = 0.009832
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 10.04, df = 4, p-value = 0.03976
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 11.371, df = 4, p-value = 0.0227
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 34.42, df = 5, p-value = 1.964e-06
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 28.308, df = 13, p-value = 0.008195
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 0.18017, df = 1, p-value = 0.6712
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 14.354, df = 4, p-value = 0.006248
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 10.368, df = 4, p-value = 0.03467
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 34.862, df = 5, p-value = 1.603e-06
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 28.459, df = 13, p-value = 0.007804
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 14.473, df = 1, p-value = 0.0001422
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 11.976, df = 4, p-value = 0.01753
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 15.041, df = 4, p-value = 0.004617
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 38.478, df = 5, p-value = 3.024e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 29.336, df = 13, p-value = 0.005863
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 9.3425, df = 1, p-value = 0.002239
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 20.042, df = 4, p-value = 0.0004899
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 11.506, df = 4, p-value = 0.02143
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 36.513, df = 5, p-value = 7.498e-07
## 
## 
##  studentized Breusch-Pagan test
## 
## data:  .
## BP = 31.348, df = 13, p-value = 0.002998